// Copyright (c) Microsoft Corporation.
// Licensed under the MIT License.

import {
  IDataset,
  IErrorAnalysisData,
  IModelExplanationData,
  Metrics,
  IPreBuiltCohort,
  FilterMethods,
  DatasetTaskType
} from "@responsible-ai/core-ui";

export const wineData: IDataset = {
  categorical_features: [],
  feature_metadata: undefined,
  feature_names: [
    "alcohol",
    "malic_acid",
    "ash",
    "alcalinity_of_ash",
    "magnesium",
    "total_phenols",
    "flavanoids",
    "nonflavanoid_phenols",
    "proanthocyanins",
    "color_intensity",
    "hue",
    "od280/od315_of_diluted_wines",
    "proline"
  ],
  features: [
    [
      14.23, 1.71, 2.43, 15.6, 127, 2.8, 3.06, 0.28, 2.29, 5.64, 1.04, 3.92,
      1065
    ],
    [
      13.2, 1.78, 2.14, 11.2, 100, 2.65, 2.76, 0.26, 1.28, 4.38, 1.05, 3.4, 1050
    ],
    [
      13.16, 2.36, 2.67, 18.6, 101, 2.8, 3.24, 0.3, 2.81, 5.68, 1.03, 3.17, 1185
    ],
    [
      14.37, 1.95, 2.5, 16.8, 113, 3.85, 3.49, 0.24, 2.18, 7.8, 0.86, 3.45, 1480
    ],
    [13.24, 2.59, 2.87, 21, 118, 2.8, 2.69, 0.39, 1.82, 4.32, 1.04, 2.93, 735],
    [
      14.2, 1.76, 2.45, 15.2, 112, 3.27, 3.39, 0.34, 1.97, 6.75, 1.05, 2.85,
      1450
    ],
    [14.39, 1.87, 2.45, 14.6, 96, 2.5, 2.52, 0.3, 1.98, 5.25, 1.02, 3.58, 1290],
    [
      14.06, 2.15, 2.61, 17.6, 121, 2.6, 2.51, 0.31, 1.25, 5.05, 1.06, 3.58,
      1295
    ],
    [14.83, 1.64, 2.17, 14, 97, 2.8, 2.98, 0.29, 1.98, 5.2, 1.08, 2.85, 1045],
    [13.86, 1.35, 2.27, 16, 98, 2.98, 3.15, 0.22, 1.85, 7.22, 1.01, 3.55, 1045],
    [14.1, 2.16, 2.3, 18, 105, 2.95, 3.32, 0.22, 2.38, 5.75, 1.25, 3.17, 1510],
    [14.12, 1.48, 2.32, 16.8, 95, 2.2, 2.43, 0.26, 1.57, 5, 1.17, 2.82, 1280],
    [13.75, 1.73, 2.41, 16, 89, 2.6, 2.76, 0.29, 1.81, 5.6, 1.15, 2.9, 1320],
    [14.75, 1.73, 2.39, 11.4, 91, 3.1, 3.69, 0.43, 2.81, 5.4, 1.25, 2.73, 1150],
    [14.38, 1.87, 2.38, 12, 102, 3.3, 3.64, 0.29, 2.96, 7.5, 1.2, 3, 1547],
    [13.63, 1.81, 2.7, 17.2, 112, 2.85, 2.91, 0.3, 1.46, 7.3, 1.28, 2.88, 1310],
    [14.3, 1.92, 2.72, 20, 120, 2.8, 3.14, 0.33, 1.97, 6.2, 1.07, 2.65, 1280],
    [13.83, 1.57, 2.62, 20, 115, 2.95, 3.4, 0.4, 1.72, 6.6, 1.13, 2.57, 1130],
    [
      14.19, 1.59, 2.48, 16.5, 108, 3.3, 3.93, 0.32, 1.86, 8.7, 1.23, 2.82, 1680
    ],
    [13.64, 3.1, 2.56, 15.2, 116, 2.7, 3.03, 0.17, 1.66, 5.1, 0.96, 3.36, 845],
    [14.06, 1.63, 2.28, 16, 126, 3, 3.17, 0.24, 2.1, 5.65, 1.09, 3.71, 780],
    [12.93, 3.8, 2.65, 18.6, 102, 2.41, 2.41, 0.25, 1.98, 4.5, 1.03, 3.52, 770],
    [13.71, 1.86, 2.36, 16.6, 101, 2.61, 2.88, 0.27, 1.69, 3.8, 1.11, 4, 1035],
    [
      12.85, 1.6, 2.52, 17.8, 95, 2.48, 2.37, 0.26, 1.46, 3.93, 1.09, 3.63, 1015
    ],
    [13.5, 1.81, 2.61, 20, 96, 2.53, 2.61, 0.28, 1.66, 3.52, 1.12, 3.82, 845],
    [13.05, 2.05, 3.22, 25, 124, 2.63, 2.68, 0.47, 1.92, 3.58, 1.13, 3.2, 830],
    [
      13.39, 1.77, 2.62, 16.1, 93, 2.85, 2.94, 0.34, 1.45, 4.8, 0.92, 3.22, 1195
    ],
    [13.3, 1.72, 2.14, 17, 94, 2.4, 2.19, 0.27, 1.35, 3.95, 1.02, 2.77, 1285],
    [13.87, 1.9, 2.8, 19.4, 107, 2.95, 2.97, 0.37, 1.76, 4.5, 1.25, 3.4, 915],
    [14.02, 1.68, 2.21, 16, 96, 2.65, 2.33, 0.26, 1.98, 4.7, 1.04, 3.59, 1035],
    [13.73, 1.5, 2.7, 22.5, 101, 3, 3.25, 0.29, 2.38, 5.7, 1.19, 2.71, 1285],
    [
      13.58, 1.66, 2.36, 19.1, 106, 2.86, 3.19, 0.22, 1.95, 6.9, 1.09, 2.88,
      1515
    ],
    [
      13.68, 1.83, 2.36, 17.2, 104, 2.42, 2.69, 0.42, 1.97, 3.84, 1.23, 2.87,
      990
    ],
    [13.76, 1.53, 2.7, 19.5, 132, 2.95, 2.74, 0.5, 1.35, 5.4, 1.25, 3, 1235],
    [13.51, 1.8, 2.65, 19, 110, 2.35, 2.53, 0.29, 1.54, 4.2, 1.1, 2.87, 1095],
    [13.48, 1.81, 2.41, 20.5, 100, 2.7, 2.98, 0.26, 1.86, 5.1, 1.04, 3.47, 920],
    [13.28, 1.64, 2.84, 15.5, 110, 2.6, 2.68, 0.34, 1.36, 4.6, 1.09, 2.78, 880],
    [13.05, 1.65, 2.55, 18, 98, 2.45, 2.43, 0.29, 1.44, 4.25, 1.12, 2.51, 1105],
    [13.07, 1.5, 2.1, 15.5, 98, 2.4, 2.64, 0.28, 1.37, 3.7, 1.18, 2.69, 1020],
    [14.22, 3.99, 2.51, 13.2, 128, 3, 3.04, 0.2, 2.08, 5.1, 0.89, 3.53, 760],
    [
      13.56, 1.71, 2.31, 16.2, 117, 3.15, 3.29, 0.34, 2.34, 6.13, 0.95, 3.38,
      795
    ],
    [13.41, 3.84, 2.12, 18.8, 90, 2.45, 2.68, 0.27, 1.48, 4.28, 0.91, 3, 1035],
    [13.88, 1.89, 2.59, 15, 101, 3.25, 3.56, 0.17, 1.7, 5.43, 0.88, 3.56, 1095],
    [13.24, 3.98, 2.29, 17.5, 103, 2.64, 2.63, 0.32, 1.66, 4.36, 0.82, 3, 680],
    [13.05, 1.77, 2.1, 17, 107, 3, 3, 0.28, 2.03, 5.04, 0.88, 3.35, 885],
    [
      14.21, 4.04, 2.44, 18.9, 111, 2.85, 2.65, 0.3, 1.25, 5.24, 0.87, 3.33,
      1080
    ],
    [14.38, 3.59, 2.28, 16, 102, 3.25, 3.17, 0.27, 2.19, 4.9, 1.04, 3.44, 1065],
    [13.9, 1.68, 2.12, 16, 101, 3.1, 3.39, 0.21, 2.14, 6.1, 0.91, 3.33, 985],
    [14.1, 2.02, 2.4, 18.8, 103, 2.75, 2.92, 0.32, 2.38, 6.2, 1.07, 2.75, 1060],
    [
      13.94, 1.73, 2.27, 17.4, 108, 2.88, 3.54, 0.32, 2.08, 8.9, 1.12, 3.1, 1260
    ],
    [
      13.05, 1.73, 2.04, 12.4, 92, 2.72, 3.27, 0.17, 2.91, 7.2, 1.12, 2.91, 1150
    ],
    [13.83, 1.65, 2.6, 17.2, 94, 2.45, 2.99, 0.22, 2.29, 5.6, 1.24, 3.37, 1265],
    [
      13.82, 1.75, 2.42, 14, 111, 3.88, 3.74, 0.32, 1.87, 7.05, 1.01, 3.26, 1190
    ],
    [13.77, 1.9, 2.68, 17.1, 115, 3, 2.79, 0.39, 1.68, 6.3, 1.13, 2.93, 1375],
    [13.74, 1.67, 2.25, 16.4, 118, 2.6, 2.9, 0.21, 1.62, 5.85, 0.92, 3.2, 1060],
    [
      13.56, 1.73, 2.46, 20.5, 116, 2.96, 2.78, 0.2, 2.45, 6.25, 0.98, 3.03,
      1120
    ],
    [14.22, 1.7, 2.3, 16.3, 118, 3.2, 3, 0.26, 2.03, 6.38, 0.94, 3.31, 970],
    [13.29, 1.97, 2.68, 16.8, 102, 3, 3.23, 0.31, 1.66, 6, 1.07, 2.84, 1270],
    [13.72, 1.43, 2.5, 16.7, 108, 3.4, 3.67, 0.19, 2.04, 6.8, 0.89, 2.87, 1285],
    [
      12.37, 0.94, 1.36, 10.6, 88, 1.98, 0.57, 0.28, 0.42, 1.95, 1.05, 1.82, 520
    ],
    [12.33, 1.1, 2.28, 16, 101, 2.05, 1.09, 0.63, 0.41, 3.27, 1.25, 1.67, 680],
    [
      12.64, 1.36, 2.02, 16.8, 100, 2.02, 1.41, 0.53, 0.62, 5.75, 0.98, 1.59,
      450
    ],
    [13.67, 1.25, 1.92, 18, 94, 2.1, 1.79, 0.32, 0.73, 3.8, 1.23, 2.46, 630],
    [12.37, 1.13, 2.16, 19, 87, 3.5, 3.1, 0.19, 1.87, 4.45, 1.22, 2.87, 420],
    [12.17, 1.45, 2.53, 19, 104, 1.89, 1.75, 0.45, 1.03, 2.95, 1.45, 2.23, 355],
    [12.37, 1.21, 2.56, 18.1, 98, 2.42, 2.65, 0.37, 2.08, 4.6, 1.19, 2.3, 678],
    [13.11, 1.01, 1.7, 15, 78, 2.98, 3.18, 0.26, 2.28, 5.3, 1.12, 3.18, 502],
    [12.37, 1.17, 1.92, 19.6, 78, 2.11, 2, 0.27, 1.04, 4.68, 1.12, 3.48, 510],
    [13.34, 0.94, 2.36, 17, 110, 2.53, 1.3, 0.55, 0.42, 3.17, 1.02, 1.93, 750],
    [
      12.21, 1.19, 1.75, 16.8, 151, 1.85, 1.28, 0.14, 2.5, 2.85, 1.28, 3.07, 718
    ],
    [
      12.29, 1.61, 2.21, 20.4, 103, 1.1, 1.02, 0.37, 1.46, 3.05, 0.91, 1.82, 870
    ],
    [13.86, 1.51, 2.67, 25, 86, 2.95, 2.86, 0.21, 1.87, 3.38, 1.36, 3.16, 410],
    [13.49, 1.66, 2.24, 24, 87, 1.88, 1.84, 0.27, 1.03, 3.74, 0.98, 2.78, 472],
    [12.99, 1.67, 2.6, 30, 139, 3.3, 2.89, 0.21, 1.96, 3.35, 1.31, 3.5, 985],
    [11.96, 1.09, 2.3, 21, 101, 3.38, 2.14, 0.13, 1.65, 3.21, 0.99, 3.13, 886],
    [11.66, 1.88, 1.92, 16, 97, 1.61, 1.57, 0.34, 1.15, 3.8, 1.23, 2.14, 428],
    [13.03, 0.9, 1.71, 16, 86, 1.95, 2.03, 0.24, 1.46, 4.6, 1.19, 2.48, 392],
    [11.84, 2.89, 2.23, 18, 112, 1.72, 1.32, 0.43, 0.95, 2.65, 0.96, 2.52, 500],
    [12.33, 0.99, 1.95, 14.8, 136, 1.9, 1.85, 0.35, 2.76, 3.4, 1.06, 2.31, 750],
    [12.7, 3.87, 2.4, 23, 101, 2.83, 2.55, 0.43, 1.95, 2.57, 1.19, 3.13, 463],
    [12, 0.92, 2, 19, 86, 2.42, 2.26, 0.3, 1.43, 2.5, 1.38, 3.12, 278],
    [12.72, 1.81, 2.2, 18.8, 86, 2.2, 2.53, 0.26, 1.77, 3.9, 1.16, 3.14, 714],
    [12.08, 1.13, 2.51, 24, 78, 2, 1.58, 0.4, 1.4, 2.2, 1.31, 2.72, 630],
    [13.05, 3.86, 2.32, 22.5, 85, 1.65, 1.59, 0.61, 1.62, 4.8, 0.84, 2.01, 515],
    [11.84, 0.89, 2.58, 18, 94, 2.2, 2.21, 0.22, 2.35, 3.05, 0.79, 3.08, 520],
    [12.67, 0.98, 2.24, 18, 99, 2.2, 1.94, 0.3, 1.46, 2.62, 1.23, 3.16, 450],
    [
      12.16, 1.61, 2.31, 22.8, 90, 1.78, 1.69, 0.43, 1.56, 2.45, 1.33, 2.26, 495
    ],
    [11.65, 1.67, 2.62, 26, 88, 1.92, 1.61, 0.4, 1.34, 2.6, 1.36, 3.21, 562],
    [11.64, 2.06, 2.46, 21.6, 84, 1.95, 1.69, 0.48, 1.35, 2.8, 1, 2.75, 680],
    [12.08, 1.33, 2.3, 23.6, 70, 2.2, 1.59, 0.42, 1.38, 1.74, 1.07, 3.21, 625],
    [12.08, 1.83, 2.32, 18.5, 81, 1.6, 1.5, 0.52, 1.64, 2.4, 1.08, 2.27, 480],
    [12, 1.51, 2.42, 22, 86, 1.45, 1.25, 0.5, 1.63, 3.6, 1.05, 2.65, 450],
    [
      12.69, 1.53, 2.26, 20.7, 80, 1.38, 1.46, 0.58, 1.62, 3.05, 0.96, 2.06, 495
    ],
    [12.29, 2.83, 2.22, 18, 88, 2.45, 2.25, 0.25, 1.99, 2.15, 1.15, 3.3, 290],
    [11.62, 1.99, 2.28, 18, 98, 3.02, 2.26, 0.17, 1.35, 3.25, 1.16, 2.96, 345],
    [12.47, 1.52, 2.2, 19, 162, 2.5, 2.27, 0.32, 3.28, 2.6, 1.16, 2.63, 937],
    [11.81, 2.12, 2.74, 21.5, 134, 1.6, 0.99, 0.14, 1.56, 2.5, 0.95, 2.26, 625],
    [12.29, 1.41, 1.98, 16, 85, 2.55, 2.5, 0.29, 1.77, 2.9, 1.23, 2.74, 428],
    [12.37, 1.07, 2.1, 18.5, 88, 3.52, 3.75, 0.24, 1.95, 4.5, 1.04, 2.77, 660],
    [12.29, 3.17, 2.21, 18, 88, 2.85, 2.99, 0.45, 2.81, 2.3, 1.42, 2.83, 406],
    [12.08, 2.08, 1.7, 17.5, 97, 2.23, 2.17, 0.26, 1.4, 3.3, 1.27, 2.96, 710],
    [12.6, 1.34, 1.9, 18.5, 88, 1.45, 1.36, 0.29, 1.35, 2.45, 1.04, 2.77, 562],
    [12.34, 2.45, 2.46, 21, 98, 2.56, 2.11, 0.34, 1.31, 2.8, 0.8, 3.38, 438],
    [11.82, 1.72, 1.88, 19.5, 86, 2.5, 1.64, 0.37, 1.42, 2.06, 0.94, 2.44, 415],
    [12.51, 1.73, 1.98, 20.5, 85, 2.2, 1.92, 0.32, 1.48, 2.94, 1.04, 3.57, 672],
    [12.42, 2.55, 2.27, 22, 90, 1.68, 1.84, 0.66, 1.42, 2.7, 0.86, 3.3, 315],
    [12.25, 1.73, 2.12, 19, 80, 1.65, 2.03, 0.37, 1.63, 3.4, 1, 3.17, 510],
    [12.72, 1.75, 2.28, 22.5, 84, 1.38, 1.76, 0.48, 1.63, 3.3, 0.88, 2.42, 488],
    [12.22, 1.29, 1.94, 19, 92, 2.36, 2.04, 0.39, 2.08, 2.7, 0.86, 3.02, 312],
    [11.61, 1.35, 2.7, 20, 94, 2.74, 2.92, 0.29, 2.49, 2.65, 0.96, 3.26, 680],
    [
      11.46, 3.74, 1.82, 19.5, 107, 3.18, 2.58, 0.24, 3.58, 2.9, 0.75, 2.81, 562
    ],
    [12.52, 2.43, 2.17, 21, 88, 2.55, 2.27, 0.26, 1.22, 2, 0.9, 2.78, 325],
    [11.76, 2.68, 2.92, 20, 103, 1.75, 2.03, 0.6, 1.05, 3.8, 1.23, 2.5, 607],
    [11.41, 0.74, 2.5, 21, 88, 2.48, 2.01, 0.42, 1.44, 3.08, 1.1, 2.31, 434],
    [12.08, 1.39, 2.5, 22.5, 84, 2.56, 2.29, 0.43, 1.04, 2.9, 0.93, 3.19, 385],
    [11.03, 1.51, 2.2, 21.5, 85, 2.46, 2.17, 0.52, 2.01, 1.9, 1.71, 2.87, 407],
    [11.82, 1.47, 1.99, 20.8, 86, 1.98, 1.6, 0.3, 1.53, 1.95, 0.95, 3.33, 495],
    [12.42, 1.61, 2.19, 22.5, 108, 2, 2.09, 0.34, 1.61, 2.06, 1.06, 2.96, 345],
    [12.77, 3.43, 1.98, 16, 80, 1.63, 1.25, 0.43, 0.83, 3.4, 0.7, 2.12, 372],
    [12, 3.43, 2, 19, 87, 2, 1.64, 0.37, 1.87, 1.28, 0.93, 3.05, 564],
    [11.45, 2.4, 2.42, 20, 96, 2.9, 2.79, 0.32, 1.83, 3.25, 0.8, 3.39, 625],
    [11.56, 2.05, 3.23, 28.5, 119, 3.18, 5.08, 0.47, 1.87, 6, 0.93, 3.69, 465],
    [
      12.42, 4.43, 2.73, 26.5, 102, 2.2, 2.13, 0.43, 1.71, 2.08, 0.92, 3.12, 365
    ],
    [13.05, 5.8, 2.13, 21.5, 86, 2.62, 2.65, 0.3, 2.01, 2.6, 0.73, 3.1, 380],
    [11.87, 4.31, 2.39, 21, 82, 2.86, 3.03, 0.21, 2.91, 2.8, 0.75, 3.64, 380],
    [12.07, 2.16, 2.17, 21, 85, 2.6, 2.65, 0.37, 1.35, 2.76, 0.86, 3.28, 378],
    [
      12.43, 1.53, 2.29, 21.5, 86, 2.74, 3.15, 0.39, 1.77, 3.94, 0.69, 2.84, 352
    ],
    [11.79, 2.13, 2.78, 28.5, 92, 2.13, 2.24, 0.58, 1.76, 3, 0.97, 2.44, 466],
    [12.37, 1.63, 2.3, 24.5, 88, 2.22, 2.45, 0.4, 1.9, 2.12, 0.89, 2.78, 342],
    [12.04, 4.3, 2.38, 22, 80, 2.1, 1.75, 0.42, 1.35, 2.6, 0.79, 2.57, 580],
    [12.86, 1.35, 2.32, 18, 122, 1.51, 1.25, 0.21, 0.94, 4.1, 0.76, 1.29, 630],
    [12.88, 2.99, 2.4, 20, 104, 1.3, 1.22, 0.24, 0.83, 5.4, 0.74, 1.42, 530],
    [12.81, 2.31, 2.4, 24, 98, 1.15, 1.09, 0.27, 0.83, 5.7, 0.66, 1.36, 560],
    [12.7, 3.55, 2.36, 21.5, 106, 1.7, 1.2, 0.17, 0.84, 5, 0.78, 1.29, 600],
    [12.51, 1.24, 2.25, 17.5, 85, 2, 0.58, 0.6, 1.25, 5.45, 0.75, 1.51, 650],
    [12.6, 2.46, 2.2, 18.5, 94, 1.62, 0.66, 0.63, 0.94, 7.1, 0.73, 1.58, 695],
    [12.25, 4.72, 2.54, 21, 89, 1.38, 0.47, 0.53, 0.8, 3.85, 0.75, 1.27, 720],
    [12.53, 5.51, 2.64, 25, 96, 1.79, 0.6, 0.63, 1.1, 5, 0.82, 1.69, 515],
    [13.49, 3.59, 2.19, 19.5, 88, 1.62, 0.48, 0.58, 0.88, 5.7, 0.81, 1.82, 580],
    [12.84, 2.96, 2.61, 24, 101, 2.32, 0.6, 0.53, 0.81, 4.92, 0.89, 2.15, 590],
    [12.93, 2.81, 2.7, 21, 96, 1.54, 0.5, 0.53, 0.75, 4.6, 0.77, 2.31, 600],
    [13.36, 2.56, 2.35, 20, 89, 1.4, 0.5, 0.37, 0.64, 5.6, 0.7, 2.47, 780],
    [13.52, 3.17, 2.72, 23.5, 97, 1.55, 0.52, 0.5, 0.55, 4.35, 0.89, 2.06, 520],
    [13.62, 4.95, 2.35, 20, 92, 2, 0.8, 0.47, 1.02, 4.4, 0.91, 2.05, 550],
    [12.25, 3.88, 2.2, 18.5, 112, 1.38, 0.78, 0.29, 1.14, 8.21, 0.65, 2, 855],
    [13.16, 3.57, 2.15, 21, 102, 1.5, 0.55, 0.43, 1.3, 4, 0.6, 1.68, 830],
    [13.88, 5.04, 2.23, 20, 80, 0.98, 0.34, 0.4, 0.68, 4.9, 0.58, 1.33, 415],
    [12.87, 4.61, 2.48, 21.5, 86, 1.7, 0.65, 0.47, 0.86, 7.65, 0.54, 1.86, 625],
    [
      13.32, 3.24, 2.38, 21.5, 92, 1.93, 0.76, 0.45, 1.25, 8.42, 0.55, 1.62, 650
    ],
    [13.08, 3.9, 2.36, 21.5, 113, 1.41, 1.39, 0.34, 1.14, 9.4, 0.57, 1.33, 550],
    [13.5, 3.12, 2.62, 24, 123, 1.4, 1.57, 0.22, 1.25, 8.6, 0.59, 1.3, 500],
    [12.79, 2.67, 2.48, 22, 112, 1.48, 1.36, 0.24, 1.26, 10.8, 0.48, 1.47, 480],
    [13.11, 1.9, 2.75, 25.5, 116, 2.2, 1.28, 0.26, 1.56, 7.1, 0.61, 1.33, 425],
    [13.23, 3.3, 2.28, 18.5, 98, 1.8, 0.83, 0.61, 1.87, 10.52, 0.56, 1.51, 675],
    [12.58, 1.29, 2.1, 20, 103, 1.48, 0.58, 0.53, 1.4, 7.6, 0.58, 1.55, 640],
    [13.17, 5.19, 2.32, 22, 93, 1.74, 0.63, 0.61, 1.55, 7.9, 0.6, 1.48, 725],
    [13.84, 4.12, 2.38, 19.5, 89, 1.8, 0.83, 0.48, 1.56, 9.01, 0.57, 1.64, 480],
    [12.45, 3.03, 2.64, 27, 97, 1.9, 0.58, 0.63, 1.14, 7.5, 0.67, 1.73, 880],
    [14.34, 1.68, 2.7, 25, 98, 2.8, 1.31, 0.53, 2.7, 13, 0.57, 1.96, 660],
    [13.48, 1.67, 2.64, 22.5, 89, 2.6, 1.1, 0.52, 2.29, 11.75, 0.57, 1.78, 620],
    [12.36, 3.83, 2.38, 21, 88, 2.3, 0.92, 0.5, 1.04, 7.65, 0.56, 1.58, 520],
    [13.69, 3.26, 2.54, 20, 107, 1.83, 0.56, 0.5, 0.8, 5.88, 0.96, 1.82, 680],
    [12.85, 3.27, 2.58, 22, 106, 1.65, 0.6, 0.6, 0.96, 5.58, 0.87, 2.11, 570],
    [12.96, 3.45, 2.35, 18.5, 106, 1.39, 0.7, 0.4, 0.94, 5.28, 0.68, 1.75, 675],
    [13.78, 2.76, 2.3, 22, 90, 1.35, 0.68, 0.41, 1.03, 9.58, 0.7, 1.68, 615],
    [
      13.73, 4.36, 2.26, 22.5, 88, 1.28, 0.47, 0.52, 1.15, 6.62, 0.78, 1.75, 520
    ],
    [13.45, 3.7, 2.6, 23, 111, 1.7, 0.92, 0.43, 1.46, 10.68, 0.85, 1.56, 695],
    [12.82, 3.37, 2.3, 19.5, 88, 1.48, 0.66, 0.4, 0.97, 10.26, 0.72, 1.75, 685],
    [
      13.58, 2.58, 2.69, 24.5, 105, 1.55, 0.84, 0.39, 1.54, 8.66, 0.74, 1.8, 750
    ],
    [13.4, 4.6, 2.86, 25, 112, 1.98, 0.96, 0.27, 1.11, 8.5, 0.67, 1.92, 630],
    [12.2, 3.03, 2.32, 19, 96, 1.25, 0.49, 0.4, 0.73, 5.5, 0.66, 1.83, 510],
    [12.77, 2.39, 2.28, 19.5, 86, 1.39, 0.51, 0.48, 0.64, 9.9, 0.57, 1.63, 470],
    [14.16, 2.51, 2.48, 20, 91, 1.68, 0.7, 0.44, 1.24, 9.7, 0.62, 1.71, 660],
    [13.71, 5.65, 2.45, 20.5, 95, 1.68, 0.61, 0.52, 1.06, 7.7, 0.64, 1.74, 740],
    [13.4, 3.91, 2.48, 23, 102, 1.8, 0.75, 0.43, 1.41, 7.3, 0.7, 1.56, 750],
    [13.27, 4.28, 2.26, 20, 120, 1.59, 0.69, 0.43, 1.35, 10.2, 0.59, 1.56, 835],
    [13.17, 2.59, 2.37, 20, 120, 1.65, 0.68, 0.53, 1.46, 9.3, 0.6, 1.62, 840],
    [14.13, 4.1, 2.74, 24.5, 96, 2.05, 0.76, 0.56, 1.35, 9.2, 0.61, 1.6, 560]
  ],
  predicted_y: [
    0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 2
  ],
  task_type: DatasetTaskType.Classification,
  true_y: [
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
    0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
    1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
    2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
    2, 2, 2
  ]
};

export const wineWithFairnessModelExplanationData: Omit<
  IModelExplanationData,
  "predictedY" | "probabilityY"
> = {
  explanationMethod: "mimic.lightgbm",
  modelClass: "blackbox",
  precomputedExplanations: {
    globalFeatureImportance: {
      intercept: 1,
      scores: [
        0.32974755301219827, 0.1599140494234892, 0.07073032886997299,
        0.05048794983537208, 0.12171766998707456, 0.05570327134282871,
        0.23660456159275034, 0, 0, 0.42534055249262703, 0.0691387123063596,
        0.09087638231829143, 0.22491629207954977
      ]
    },
    localFeatureImportance: {
      scores: [
        [
          [
            0.05216083488120387, -0.22871973175861998, -0.4043641901572746,
            -0.0015613394518991588, -0.35359431570043803, 0.023682705506317465,
            0, 0.06714644555098671, -0.2560164587214607, -0.15177641898879637,
            0.9792139141527457, -0.0017877274553669484, 0.24061343435263205
          ],
          [
            -0.12092261775932218, -0.22914111021191233, -0.40078795920685817,
            0.0005815777442380876, -0.3530546629047623, -0.054326977200834806,
            0.000001933635349879448, 0.06707299188127282, 0.11827229570681258,
            -0.27331182473353605, 0.9768285580109123, 0.0008917382811926208,
            -0.09072815344128085
          ],
          [
            -0.04054074095095876, -0.2285281655248415, 1.423294882727287,
            0.00028985445969838177, 1.0439181517231315, 0.02367676355780154, 0,
            0.06716015625829438, -0.1631992688457688, -0.15260204403162972,
            -0.702222044334975, 0.0005125017996215776, -0.09134411508433998
          ],
          [
            0.053337268497305475, -0.22848586865921944, -0.3993579061314111,
            0.000691075322251565, -0.35327813426564963, 0.023757517954573076, 0,
            0.06708562502171213, -0.2564268623881123, -0.15176793729201074,
            -0.700498637064953, -0.00005301328487862869, 0.24011999186212182
          ],
          [
            0.0530776357541605, 0.7175439538429149, 1.4265444601034647,
            0.0002774577101441302, 1.0439675219152424, 0.02368234556404682, 0,
            -0.03310357247558648, -0.385288182677199, 0.19262751043726797,
            0.9794333000440835, 0.000011443725517016126, -0.09175234493224446
          ],
          [
            -0.04030306587020242, -0.2285321535983945, -0.3989451173652621,
            0.000691075322251565, -0.3532954975109716, 0.023757517954573076,
            -0.000002442875635721986, -0.033019247254733486,
            -0.1629302188700364, -0.15156854758466098, -0.7005117889880002,
            -0.0018789222226863293, -0.09070455484470388
          ],
          [
            0.15676149784644539, -0.22889154982897772, -0.39951931388954753,
            0.0006905046838448312, -0.35353071264317304, 0.070498989590971, 0,
            -0.0330958390835713, 0.25091482016307454, -0.2737319172331678,
            -0.7003676453663142, -0.0017877274553669484, 0.24011999186212182
          ],
          [
            -0.1209278873468164, 0.7169271604333225, -0.4011313646347173,
            0.0005579871564261657, 1.043806464992482, 0.07049578739323026, 0,
            -0.033012817536299366, 0.15915975724434003, -0.27331273284804786,
            0.9768285580109123, 0.0005735758842521522, -0.09141926937435059
          ],
          [
            -0.04054074095095876, -0.22852642813323543, 1.4234623286295214,
            0.0002715226399286992, 1.0446198934734119, -0.018257046592395593, 0,
            -0.033026520671326665, -0.2493458258645594, 0.19365834175218113,
            -0.7020779007132891, -0.0007669834668744876, -0.09134411508433998
          ],
          [
            -0.04030833545769666, -0.228564382189244, -0.39920247660811664,
            0.0005366830306034022, 1.0437526614620225, -0.018385937359001635, 0,
            -0.033011878844795936, -0.24947493582043834, 0.1923234551056284,
            -0.7003676453663142, 0.00037657314178719207, -0.09118143948139468
          ],
          [
            0.15798187955673554, -0.22898021437001018, -0.40103556548771463,
            0.0004926639113313628, 1.043806464992482, -0.05433017939857555, 0,
            -0.033012817536299366, 0.20988279424494527, -0.27334108156388237,
            0.9768285580109123, 0.0005735758842521522, -0.09102618650109212
          ],
          [
            0.15693822607964297, 0.7174901147010924, -0.40423315414111105,
            0.0004925086976847294, 1.0438190573996073, -0.05433017939857555, 0,
            -0.033012817536299366, 0.21028685960230636, -0.2737462131715116,
            0.979232257624364, 0.0005735758842521522, -0.09149578319501935
          ],
          [
            0.05205779694213367, -0.2287175956327428, -0.39976581699960656,
            -0.0015822164909287117, -0.35331736025304694, 0.02366917604215916,
            0, -0.03296541701013523, -0.25644144328166535, -0.15155087263659306,
            -0.7005117889880002, 0.0008110971340095242, 0.24011999186212182
          ],
          [
            -0.04054074095095876, -0.2285281655248415, 1.423294882727287,
            0.00028985445969838177, 1.0439181517231315, 0.02367676355780154, 0,
            0.06716015625829438, -0.1631992688457688, -0.15260204403162972,
            -0.702222044334975, 0.0005125017996215776, -0.09134411508433998
          ],
          [
            0.15796272590042956, -0.22914256861797985, -0.40078795920685817,
            0.0005815777442380876, -0.3530216227393197, -0.054326977200834806,
            0.000001933635349879448, -0.03295920399024703, 0.20988338452286243,
            -0.27331182473353605, 0.9768285580109123, 0.0009297361009090316,
            -0.09072815344128085
          ],
          [
            -0.12108204933889084, -0.22864562094757582, 1.4233475999379939,
            0.00028985445969838177, 1.0439115747915853, 0.07040986021549289, 0,
            -0.033096629181891414, 0.28529780023189033, 0.3256959872556782,
            -0.7020779007132891, 0.000011443725517016126, -0.09150601943758888
          ],
          [
            0.1568645357855156, -0.2287108859010076, -0.3995068714337223,
            0.000691075322251565, -0.35324487364209906, 0.07048503260601915, 0,
            -0.03295826529874361, 0.2507894498780269, -0.2737292163767784,
            -0.7003544934432671, -0.000015015465162221409, 0.24011999186212182
          ],
          [
            0.05205779694213367, -0.22866775676645867, -0.39976581699960656,
            -0.0015822164909287117, -0.35334952160765487, 0.02366917604215916,
            0, -0.03317741444589703, -0.25644144328166535, -0.15155087263659306,
            -0.700498637064953, 0.0007730993142931134, 0.24011999186212182
          ],
          [
            -0.12108057052184705, -0.2286329607449248, 1.424072465085392,
            -0.0008181208423758135, -0.35309251681937687, 0.07038784328856108,
            0.000001933635349879448, -0.03295826529874361, 0.1591847749532909,
            -0.2733411107298454, -0.7020779007132891, 0.0009297361009090316,
            -0.09107433337618522
          ],
          [
            0.052071234390244005, -0.2286681061363025, -0.399752325682481,
            -0.0011419679708839607, -0.35354438149387757, 0.023683133027111017,
            0, 0.06716129112650993, -0.25642655862575203, -0.15176793729201074,
            -0.700498637064953, -0.0017877274553669484, 0.24011999186212182
          ],
          [
            0.053099593416549136, -0.22854912193480226, 1.424123070790393,
            -0.0006650360344139629, 1.0448229145165369, 0.023651971953922536, 0,
            0.06718065481543739, -0.25642655862575203, -0.15159805935529644,
            -0.702222044334975, -0.0010672330152308, -0.09134411508433998
          ],
          [
            0.1580409694016172, -0.22860332408195375, -0.39935737368793195,
            0.0006905046838448312, -0.3532582466429449, 0.070498989590971, 0,
            -0.03317026273450541, 0.3769030654345434, 0.32625099575582717,
            -0.7003544934432671, 0.0006947355387276598, 0.24011999186212182
          ],
          [
            0.05325902499611782, -0.22929397063080273, -0.399028140208969,
            0.0006463303509261584, -0.35304278231604946, 0.023757517954573076,
            -0.000002442875635721986, -0.032965653122721976,
            -0.3849385526111283, 0.1936859639764061, -0.7003676453663142,
            0.00031003905990496274, -0.09072900842171255
          ],
          [
            -0.12108204933889084, -0.22864562094757582, 1.4233475999379939,
            0.00028985445969838177, 1.0446437647474243, 0.07040986021549289, 0,
            -0.033076130624748415, 0.28529780023189033, 0.3256959872556782,
            -0.7020779007132891, -0.0007421784901636194, -0.09150591229137754
          ],
          [
            -0.12084437425813456, -0.22841666043819356, -0.40685096817939614,
            -0.0015602229854511946, -0.3530854468019673, 0.07041022015776353,
            0.000001933635349879448, -0.03295826529874361, 0.2852983905098075,
            0.3264227865717222, -0.7020779007132891, 0.0007327333584440723,
            -0.09091765620893454
          ],
          [
            -0.12109715304748574, -0.22906717203319385, 1.4259996795919845,
            0.0002774577101441302, 1.0439675225373362, 0.07040986021549289, 0,
            0.0671484269960978, 0.285267401010699, 0.32539193192403865,
            0.979232257624364, 0.0003154990571566183, -0.09151267081053524
          ],
          [
            -0.04030833545769666, -0.22827126489667432, -0.4041775024022737,
            0.0006020062756982051, 1.0438107940115828, 0.023762690735538897, 0,
            -0.033011878844795936, -0.2912857423475298, 0.19335428642054156,
            0.9792139141527457, 0.00032583446411948037, -0.0911874379170893
          ],
          [
            -0.04030833545769666, -0.22813282693166592, -0.39920247660811664,
            0.0005366830306034022, 1.0437526614620225, -0.018385937359001635, 0,
            -0.033011878844795936, -0.2493458258645594, 0.1923234551056284,
            -0.7003544934432671, 0.00037657314178719207, -0.09118143948139468
          ],
          [
            -0.12092261775932218, -0.22915920769440698, -0.39907482050051035,
            0.0005810071058313538, -0.35302270260171636, -0.054326977200834806,
            -0.000002442875635721986, -0.03295920399024703, 0.2445421228707245,
            0.32645038546317684, -0.7003676453663142, 0.0007327333584440723,
            -0.09072900842171255
          ],
          [
            0.15693822607964297, -0.22896857637384002, -0.4011723100045582,
            0.0004926639113313628, 1.0438178359356927, -0.05433017939857555, 0,
            -0.033012817536299366, 0.20988279424494527, -0.27334108156388237,
            0.9768285580109123, 0.0005735758842521522, -0.09102618650109212
          ],
          [
            0.15803569981412297, -0.22871119952983984, -0.4013210353872888,
            -0.0015811000244807475, -0.35361493165039165, 0.07039669069360523,
            0, -0.03301268946494989, 0.25104312724138944, -0.274135232766149,
            0.9768285580109123, -0.0018789222226863293, 0.24011999186212182
          ],
          [
            0.05205779694213367, -0.22828318518774332, -0.39962883319758746,
            -0.0015613394518991588, -0.35339357157926715, 0.02366986665267182,
            -0.000002442875635721986, 0.06716098736414966, -0.2564268623881123,
            -0.15159783358097031, -0.700498637064953, -0.0017877274553669484,
            -0.09075084095510012
          ],
          [
            -0.1209278873468164, 0.7169271604333225, -0.40117203887917546,
            0.0005578319427795323, 1.043807686456397, 0.07049578739323026, 0,
            -0.033012817536299366, 0.2852733728008568, 0.32532672987761924,
            0.9768285580109123, 0.00037657314178719207, -0.09160003476837647
          ],
          [
            0.15692478863153264, 0.7180255261981932, -0.40135216974380544,
            0.0006469009893328923, -0.3535370433643666, 0.07048503260601915, 0,
            -0.0330958390835713, 0.2507650224469932, -0.2737292163767784,
            0.9768285580109123, -0.0017877274553669484, 0.24106635560726755
          ],
          [
            0.05255842859577309, -0.22877094985193724, 1.4254290701859997,
            -0.0008181208423758135, -0.3533574124375611, 0.023647489783469373,
            0, -0.03317741444589703, -0.25644144328166535, -0.15155841705290382,
            -0.702222044334975, 0.0007730993142931134, 0.2409432788134021
          ],
          [
            0.053101072233592984, -0.2285483557422036, 1.4264694925472097,
            -0.0004099904863824628, -0.35333513275535366, 0.023647489783469373,
            -0.000002442875635721986, -0.03310205210345949,
            -0.25624605495634173, -0.15159959715688492, 0.9794333000440835,
            -0.0017877274553669484, -0.09107518835661692
          ],
          [
            0.15796272590042956, 0.7168705015799768, -0.39935406321601685,
            0.0005815777442380876, -0.3530216227393197, -0.054326977200834806,
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      ]
    }
  }
};

export const wineErrorAnalysisData: IErrorAnalysisData = {
  maxDepth: 3,
  metric: Metrics.AccuracyScore,
  minChildSamples: 20,
  numLeaves: 11
};

export const wineCohortDataContinuous: IPreBuiltCohort = {
  cohort_filter_list: [
    {
      arg: [12.09],
      column: "alcohol",
      method: FilterMethods.LessThan
    },
    {
      arg: [2.5],
      column: "ash",
      method: FilterMethods.GreaterThan
    }
  ],
  name: "Cohort Continuous"
};

export const wineCohortDataCategorical: IPreBuiltCohort = {
  cohort_filter_list: [
    {
      arg: [80, 81, 82],
      column: "magnesium",
      method: FilterMethods.Includes
    }
  ],
  name: "Cohort Categorical"
};

export const wineCohortDataIndex: IPreBuiltCohort = {
  cohort_filter_list: [
    {
      arg: [54],
      column: "Index",
      method: FilterMethods.LessThan
    }
  ],
  name: "Cohort Index"
};

export const wineCohortDataPredictedY: IPreBuiltCohort = {
  cohort_filter_list: [
    {
      arg: ["Class 0", "Class 1", "Class 2"],
      column: "Predicted Y",
      method: FilterMethods.Includes
    }
  ],
  name: "Cohort Predicted Y"
};

export const wineCohortDataTrueY: IPreBuiltCohort = {
  cohort_filter_list: [
    {
      arg: ["Class 0", "Class 1", "Class 2"],
      column: "True Y",
      method: FilterMethods.Includes
    }
  ],
  name: "Cohort True Y"
};
